Effect of time resolution on statistical modeling of cooling energy use in large commercial buildings

Conference · Sun Dec 31 04:00:00 EST 1995

OSTI ID:211799

Katipamula, S ^[1]; Reddy, T A; Claridge, D E ^[2]

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1. Pacific Northwest Lab., Richland, WA (United States)
2. Texas A and M Univ., College Station, TX (United States)

Regression models of measured energy use in commercial buildings are becoming an increasingly popular method of determining retrofit savings or identifying operational and maintenance (O and M) problems. When hourly monitored data are available, an issue that arises is what time resolution to adopt for regression models to be most accurate. This paper addresses this question by comparing monthly, daily, hourly, and individual hourly or hour-of-day (HOD) multiple linear regression (MLR) models when applied to measured cooling energy consumption ({dot E}{sub c}) in commercial buildings. {dot E}{sub c} consumption in five large commercial buildings in Texas (both under dual-duct constant-volume [DDCV] and dual-duct variable-volume [VAV] operation) is modeled in all four time scales using functional forms based on engineering principles. The relative advantages and disadvantages of all four types of models are discussed and compared. The outdoor dry-bulb and dew-point temperatures accounted for most of the variation (80% or more) in {dot E}{sub c}. Although the monthly models had higher model R{sup 2} than daily, hourly, and HOD models, the daily and HOD models proved more accurate at predicting {dot E}{sub c}. Also, the HOD models had higher model R{sup 2} and lower coefficients of variation (CV) than the hourly models. The results of this study suggest that daily time scale models are most advantageous for retrofit savings determination, while HOD models are best for O and M purposes.

• OSTI ID: 211799
• Report Number(s): CONF-950624--
• Country of Publication: United States
• Language: English

Related Subjects

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION
AIR CONDITIONERS
COMMERCIAL BUILDINGS
ENERGY CONSUMPTION
STATISTICAL MODELS
TIME RESOLUTION